96% Correct Next Token Prediction, with No DNN, No Training, Autodistilled Model
A new model for predicting the next token has been developed, achieving 96% accuracy without the use of deep neural networks or traditional training methods. This model, which incorporates auto-distillation, is designed for specialized corporate corpuses and offers increased explainability and lower costs. The approach has gained attention from researchers and is being recognized as a viable alternative to standard deep learning techniques.
- ▪The model achieves 100% correct predictions on the training set without training or deep neural networks.
- ▪It features auto-distillation, which allows the model to identify and drop non-contributing weights over time.
- ▪The model has a 96% correct prediction rate for next token prediction, significantly outperforming standard transformer-based models.
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| Original publisher | xLLM and AI Technology |
| Canonical URL | https://mltechniques.com/2026/05/25/96-correct-next-token-prediction-with-no-dnn-no-training-auto-distilled-model/ |
| Publication time | Mon, 25 May 2026 06:21:31 +0000 |
| Retrieval time | 2026-05-25T06:37:36.378Z |
| Last seen | 2026-05-25T06:37:36.378Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | DD8fqaVFxzqJ |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Over the last 12 months, I’ve built a model to predict the next token and to suggest synonyms or related queries to a user prompt, with 100% correct predictions on the training set in one shot, without training or deep neural networks (DNNs). The same model is now integrated in some of the most recent LLM architectures, albeit with costly training via DNNs. My version does not need DNNs or training. The purpose of this article is to provide validation to my deep neural network alternative in the context of LLMs. The new model is as a substitute to standard DNNs, with increased explainability and higher accuracy. It is designed for corporate corpuses. The end goal is to provide better accuracy at a much lower cost, while providing full control over all the components.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at xLLM and AI Technology.
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